Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computer-readable storage medium having instructions stored thereon that, when executed by a processor of a quality of experience assurance decision support system, cause the quality of experience assurance decision support system to perform operations comprising: determining, by a quality of experience assurance analyzer of the quality of experience assurance decision support system, whether quality of experience assurance analytics indicate that a quality of experience associated with a service provided by a virtual machine has been degraded; in response to determining that the quality of experience associated with the service provided by the virtual machine has been degraded, constructing, via the quality of experience assurance analyzer, a fault correlation information model to be utilized for root cause analysis to determine a root cause of the quality of experience associated with the service provided by the virtual machine being degraded; determining, by the quality of experience assurance analyzer, based upon the fault correlation information model, whether the root cause of the quality of experience associated with the service provided by the virtual machine being degraded is due to a capacity reduction, wherein determining, based upon the fault correlation information model, whether the root cause of the quality of experience associated with the service provided by the virtual machine being degraded is due to a capacity reduction comprises determining whether a number of suspend events associated with the virtual machine entering into a suspend state equals a number of threshold-crossing alarm events associated with a throughput of the service provided by the virtual machine being lower than a playback rate; and in response to determining that the root cause of the quality of experience associated with the service provided by the virtual machine being degraded is due to a capacity reduction, identifying, by the quality of experience assurance analyzer, a new network resource for capacity reallocation to accommodate a virtual machine migration.
This invention relates to a quality of experience (QoE) assurance system for virtualized services, specifically addressing degradation in service quality due to capacity issues. The system monitors QoE metrics associated with services provided by virtual machines (VMs) to detect performance degradation. When degradation is identified, the system constructs a fault correlation information model to analyze the root cause. The model evaluates whether the degradation stems from capacity reduction by comparing the number of VM suspend events with threshold-crossing alarm events indicating service throughput falling below a playback rate. If capacity reduction is confirmed, the system identifies new network resources for reallocating capacity to support VM migration, thereby mitigating the QoE impact. The approach automates root cause analysis and resource allocation to maintain service quality in virtualized environments.
2. The computer-readable storage medium of claim 1 , wherein determining, by the quality of experience assurance analyzer, whether the quality of experience assurance analytics indicate that the quality of experience associated with the service provided by the virtual machine has been degraded comprises: determining, by the quality of experience assurance analyzer, whether a video rebuffering frequency is greater than or equal to a video rebuffering frequency threshold; and if the video rebuffering frequency is greater than or equal to the video rebuffering frequency threshold, determining that the quality of experience associated with the service provided by the virtual machine has been degraded.
This invention relates to monitoring and ensuring the quality of experience (QoE) for services delivered by virtual machines (VMs) in a computing environment. The problem addressed is the degradation of service quality, particularly in video streaming applications, where interruptions such as rebuffering can negatively impact user experience. The solution involves analyzing QoE metrics to detect and respond to performance issues. The system includes a quality of experience assurance analyzer that evaluates analytics data to determine if the service quality has degraded. Specifically, the analyzer checks whether the video rebuffering frequency exceeds a predefined threshold. If the rebuffering frequency meets or surpasses this threshold, the system concludes that the QoE has been degraded. This allows for proactive measures to be taken to mitigate the issue, such as adjusting VM resources or optimizing network conditions. The invention focuses on real-time monitoring of video streaming performance within virtualized environments, ensuring that users receive a consistent and high-quality experience. By setting and enforcing thresholds for rebuffering events, the system helps maintain service reliability and user satisfaction. This approach is particularly useful in cloud computing and virtualized infrastructure where dynamic resource allocation and performance variability can affect service delivery.
3. The computer-readable storage medium of claim 1 , wherein the operations further comprise generating an alert directed to a software-defined network controller.
A system and method for network monitoring and alerting in a software-defined network (SDN) environment. The technology addresses the challenge of detecting and responding to network anomalies or security threats in real-time within SDN architectures. The system monitors network traffic and identifies deviations from expected behavior, such as unusual data flows, unauthorized access attempts, or performance degradation. When an anomaly is detected, the system generates an alert directed to a software-defined network controller, which manages the network's virtualized infrastructure. The controller can then take corrective actions, such as rerouting traffic, isolating affected nodes, or applying security policies to mitigate the issue. The alert includes relevant details about the anomaly, such as its location, severity, and potential impact, enabling the controller to respond effectively. This approach enhances network security and operational efficiency by automating threat detection and response within the SDN framework. The system may also log the alert and associated data for further analysis, compliance, or auditing purposes. The solution is particularly useful in dynamic network environments where rapid adaptation to changing conditions is critical.
4. The computer-readable storage medium of claim 3 , wherein the alert comprises a recommendation that identifies the new network resource for capacity reallocation to accommodate the virtual machine migration.
This invention relates to network resource management in virtualized computing environments, specifically addressing the challenge of efficiently reallocating network resources to accommodate virtual machine (VM) migrations. The system monitors network resource utilization and detects when a VM migration is required due to capacity constraints or performance optimization needs. Upon detecting such a need, the system generates an alert that includes a recommendation for reallocating network resources. The recommendation identifies a new network resource, such as a network switch, router, or bandwidth allocation, that can be reassigned to support the VM migration. The system may also analyze historical data, current load conditions, and future demand projections to determine the optimal network resource for reallocation. The alert is then transmitted to an administrator or automated management system to facilitate the reallocation process. This approach ensures that network resources are dynamically adjusted to support VM migrations without disrupting ongoing operations, improving overall system efficiency and reliability. The invention may be implemented as part of a broader network management framework that integrates with virtualization platforms to automate resource allocation decisions.
5. The computer-readable storage medium of claim 4 , wherein the operations further comprise sending the alert to the software-defined network controller so that the software-defined network controller can initiate the virtual machine migration to the new network resource identified in the recommendation.
A system for managing virtual machine (VM) migration in a software-defined network (SDN) environment addresses the challenge of efficiently relocating VMs to optimize network performance and resource utilization. The system monitors network conditions, such as traffic congestion, latency, or resource availability, to detect issues that may require VM migration. When a problem is identified, the system analyzes potential network resources to determine an optimal destination for the VM. It then generates a recommendation specifying the new network resource and sends an alert to the SDN controller. The SDN controller uses this alert to initiate the VM migration process, ensuring seamless relocation without manual intervention. This automated approach improves network efficiency by dynamically adjusting VM placement based on real-time conditions, reducing downtime and enhancing performance. The system integrates with existing SDN infrastructure, leveraging its centralized control capabilities to streamline migration decisions and execution. By automating the detection, analysis, and migration processes, the system minimizes administrative overhead and ensures timely responses to network changes.
6. The computer-readable storage medium of claim 1 , wherein identifying the new network resource for capacity reallocation to accommodate the virtual machine migration comprises selecting a hardware host from a resource pool in a server cluster associated with the virtual machine.
This invention relates to virtual machine (VM) migration and resource management in server clusters. The problem addressed is efficiently reallocating network resources to accommodate VM migrations without disrupting performance. The solution involves dynamically identifying and selecting a new network resource from a hardware host pool within a server cluster to support the migration. The selection process ensures optimal resource utilization by evaluating available capacity in the cluster. The system monitors network traffic and VM workloads to determine the most suitable hardware host for reallocation. This approach minimizes downtime and maintains service continuity during VM migrations. The invention improves resource efficiency by leveraging underutilized hosts in the cluster, reducing the need for additional infrastructure. The method also includes mechanisms to prioritize hosts based on factors like current load, network latency, and proximity to the target VM. By integrating these selection criteria, the system ensures seamless migration while optimizing network performance. The overall goal is to enhance scalability and reliability in virtualized environments by intelligently managing resource allocation during VM transitions.
7. The computer-readable storage medium of claim 1 , wherein determining whether the root cause of the quality of experience associated with the service provided by the virtual machine being degraded is due to a capacity reduction further comprises determining capacity utilization of a hardware host that hosts the virtual machine is greater than a designated utilization threshold.
This invention relates to monitoring and diagnosing performance issues in virtualized computing environments, specifically identifying root causes of degraded quality of experience (QoE) in services provided by virtual machines (VMs). The problem addressed is the difficulty in determining whether performance degradation stems from insufficient hardware resources, particularly when a VM's host system is overutilized. The invention involves a method for analyzing VM performance by evaluating hardware host capacity utilization. When QoE degradation is detected, the system checks whether the hardware host's resource usage exceeds a predefined utilization threshold. If utilization surpasses this threshold, it indicates that the root cause of the performance issue is likely due to insufficient capacity on the host system. This approach helps distinguish between hardware-related bottlenecks and other potential causes of degraded service quality, enabling more targeted troubleshooting and resource allocation decisions. The solution is implemented through automated monitoring and analysis of host resource metrics, providing a systematic way to diagnose performance issues in virtualized environments.
8. A method comprising: determining, by a quality of experience assurance analyzer of a quality of experience assurance decision support system, whether quality of experience assurance analytics indicate that a quality of experience associated with a service provided by a virtual machine has been degraded; in response to determining that the quality of experience associated with the service provided by the virtual machine has been degraded, constructing, via the quality of experience assurance analyzer, a fault correlation information model to be utilized for root cause analysis to determine a root cause of the quality of experience associated with the service provided by the virtual machine being degraded; determining, by the quality of experience assurance analyzer, based upon the fault correlation information model, whether the root cause of the quality of experience associated with the service provided by the virtual machine being degraded is due to a capacity reduction, wherein determining, based upon the fault correlation information model, whether the root cause of the quality of experience associated with the service provided by the virtual machine being degraded is due to a capacity reduction comprises determining whether a number of suspend events associated with the virtual machine entering into a suspend state equals a number of threshold-crossing alarm events associated with a throughput of the service provided by the virtual machine being lower than a playback rate; and in response to determining that the root cause of the quality of experience associated with the service provided by the virtual machine being degraded is due to a capacity reduction, identifying, by the quality of experience assurance analyzer, a new network resource for capacity reallocation to accommodate a virtual machine migration.
The invention relates to a quality of experience (QoE) assurance system for virtualized services, specifically addressing degradation in service quality due to capacity constraints. The system monitors QoE metrics associated with a service provided by a virtual machine (VM) and detects when performance falls below acceptable thresholds. Upon detecting degradation, a fault correlation information model is constructed to analyze the root cause. The system evaluates whether the degradation stems from capacity reduction by comparing the number of VM suspend events with threshold-crossing alarm events indicating that the service throughput falls below a required playback rate. If capacity reduction is identified as the root cause, the system identifies a new network resource to reallocate capacity and accommodate VM migration, thereby restoring service quality. This approach ensures proactive management of virtualized services by dynamically addressing capacity-related performance issues through automated root cause analysis and resource reallocation.
9. The method of claim 8 , wherein determining, by the quality of experience assurance analyzer, whether the quality of experience assurance analytics indicate that the quality of experience associated with the service provided by the virtual machine has been degraded comprises: determining, by the quality of experience assurance analyzer, whether a video rebuffering frequency is greater than or equal to a video rebuffering frequency threshold; and if the video rebuffering frequency is greater than or equal to the video rebuffering frequency threshold, determining that the quality of experience associated with the service provided by the virtual machine has been degraded.
This invention relates to quality of experience (QoE) monitoring in virtualized environments, specifically for detecting degradation in service quality provided by virtual machines (VMs). The system analyzes QoE metrics to assess whether a service, such as video streaming, is performing adequately. A key aspect is evaluating video rebuffering frequency—a common indicator of poor streaming performance. The system compares the observed rebuffering frequency against a predefined threshold. If the frequency meets or exceeds this threshold, the system concludes that the QoE has degraded, triggering potential corrective actions. This approach ensures real-time detection of service quality issues in virtualized systems, particularly for latency-sensitive applications like video streaming. The method leverages automated analysis to identify performance degradation without manual intervention, improving reliability and user satisfaction. The threshold-based comparison provides a clear, objective criterion for determining when service quality falls below acceptable levels. This solution is particularly valuable in cloud computing and virtualized network environments where maintaining consistent QoE is critical.
10. The method of claim 8 , further comprising generating an alert directed to a software-defined network controller.
A system and method for managing network traffic in a software-defined network (SDN) environment addresses the challenge of efficiently detecting and responding to network anomalies or security threats. The method involves monitoring network traffic flows within the SDN infrastructure, where a centralized controller dynamically configures network devices such as switches and routers. The monitoring process includes analyzing traffic patterns, identifying deviations from expected behavior, and assessing potential security risks. When an anomaly or threat is detected, the system generates an alert directed to the SDN controller. The controller then takes corrective action, such as modifying traffic routing, applying security policies, or isolating affected network segments. This approach enhances network security and performance by leveraging the programmability of SDN to respond in real time to detected issues. The method may also involve logging the alert and associated data for further analysis, ensuring a comprehensive record of network events. By integrating alert generation with the SDN controller, the system enables rapid and automated responses to network disruptions, improving overall network resilience.
11. The method of claim 10 , wherein the alert comprises a recommendation that identifies the new network resource for capacity reallocation to accommodate the virtual machine migration.
A method for optimizing network resource allocation in a virtualized computing environment addresses the challenge of efficiently managing network resources during virtual machine (VM) migrations. The method involves monitoring network performance metrics, such as bandwidth utilization, latency, and packet loss, to detect potential bottlenecks or inefficiencies. When a VM migration is initiated, the system analyzes the current network topology and identifies available network resources that can be reallocated to support the migration. The method then generates an alert that includes a recommendation for reallocating network resources, specifically identifying a new network resource that can accommodate the migration. This recommendation helps ensure that the migration proceeds smoothly without disrupting existing network operations. The method may also involve dynamically adjusting network configurations, such as bandwidth allocation or routing paths, to optimize performance during and after the migration. By proactively identifying and reallocating network resources, the method improves network efficiency and reduces the risk of performance degradation during VM migrations.
12. The method of claim 11 , further comprising sending the alert to the software-defined network controller so that the software-defined network controller can initiate the virtual machine migration to the new network resource identified in the recommendation.
A method for managing virtual machine (VM) migration in a software-defined network (SDN) environment addresses the challenge of efficiently relocating VMs to optimize network performance and resource utilization. The method involves monitoring network conditions, such as traffic patterns, latency, and resource availability, to detect potential performance issues or inefficiencies. When a problem is identified, the system generates a recommendation for migrating the affected VM to a new network resource, such as a different server or network segment, to improve performance or balance the load. The recommendation includes details about the target resource and the migration process. The method further includes sending an alert to the SDN controller, which then initiates the VM migration to the recommended new network resource. This ensures seamless and automated relocation of VMs based on real-time network analysis, enhancing overall system efficiency and reliability. The approach leverages SDN capabilities to dynamically adjust VM placement in response to changing network conditions, reducing manual intervention and improving resource management.
13. The method of claim 8 , wherein identifying the new network resource for capacity reallocation to accommodate the virtual machine migration comprises selecting a hardware host from a resource pool in a server cluster associated with the virtual machine.
This invention relates to virtual machine (VM) migration and resource allocation in server clusters. The problem addressed is efficiently reallocating network resources to accommodate VM migration while maintaining performance and minimizing disruption. The method involves identifying a new network resource to support the migration by selecting a hardware host from a resource pool within a server cluster associated with the VM. The selection process ensures the chosen host has sufficient capacity to handle the migrated VM, optimizing resource utilization and avoiding bottlenecks. The resource pool includes multiple hardware hosts, each with available compute, storage, and network resources. The method dynamically evaluates host availability, performance metrics, and compatibility with the VM's requirements to determine the best candidate for migration. This approach improves scalability and reliability in data center environments by ensuring seamless VM migration without overloading existing infrastructure. The solution is particularly useful in cloud computing and enterprise IT systems where VM mobility is critical for load balancing, maintenance, and disaster recovery.
14. The method of claim 8 , wherein determining whether the root cause of the quality of experience associated with the service provided by the virtual machine being degraded is due to a capacity reduction further comprises determining capacity utilization of a hardware host that hosts the virtual machine is greater than a designated utilization threshold.
This invention relates to monitoring and diagnosing performance issues in virtualized computing environments, specifically identifying root causes of degraded quality of experience (QoE) for services provided by virtual machines (VMs). The problem addressed is the difficulty in determining whether performance degradation stems from insufficient hardware resources, particularly when a VM's host system is overutilized. The method involves analyzing the capacity utilization of the physical hardware host that runs the VM. If the host's resource utilization exceeds a predefined threshold, it is determined that the QoE degradation is due to a capacity reduction. This threshold may be based on metrics such as CPU, memory, or storage usage. The approach helps distinguish between VM-specific issues and broader infrastructure limitations, enabling targeted remediation. By correlating QoE degradation with host-level resource constraints, the system provides actionable insights for optimizing virtualized environments. This method is part of a broader system for diagnosing and resolving performance bottlenecks in cloud and virtualized computing systems.
15. A quality of experience assurance decision support system comprising: a processor; and a memory that stores a plurality of modules comprising instructions that, when executed by the processor, cause the quality of experience assurance decision support system to perform operations comprising determining, by a quality of experience assurance analyzer module of the plurality of modules, whether quality of experience assurance analytics indicate that a quality of experience associated with a service provided by a virtual machine has been degraded, in response to determining that the quality of experience associated with the service provided by the virtual machine has been degraded, constructing, via the quality of experience assurance analyzer module, a fault correlation information model to be utilized for root cause analysis to determine a root cause of the quality of experience associated with the service provided by the virtual machine being degraded, determining, by the quality of experience assurance analyzer module, based upon the fault correlation information model, whether the root cause of the quality of experience associated with the service provided by the virtual machine being degraded is due to a capacity reduction, wherein determining, based upon the fault correlation information model, whether the root cause of the quality of experience associated with the service provided by the virtual machine being degraded is due to a capacity reduction comprises determining whether a number of suspend events associated with the virtual machine entering into a suspend state equals a number of threshold-crossing alarm events associated with a throughput of the service provided by the virtual machine being lower than a playback rate, and in response to determining that the root cause of the quality of experience associated with the service provided by the virtual machine being degraded is due to a capacity reduction, identifying, by the quality of experience assurance analyzer module, a new network resource for capacity reallocation to accommodate a virtual machine migration.
A quality of experience assurance decision support system monitors and analyzes the performance of services provided by virtual machines to detect degradation in user experience. The system includes a processor and memory storing modules that perform operations to assess quality of experience metrics. If degradation is detected, the system constructs a fault correlation information model to identify the root cause. The analysis determines whether the degradation stems from capacity reduction by comparing the number of suspend events (when the virtual machine enters a suspend state) with threshold-crossing alarm events (indicating service throughput falling below a playback rate). If capacity reduction is confirmed, the system identifies a new network resource for reallocating capacity to support virtual machine migration, thereby mitigating performance issues. This approach ensures continuous service quality by dynamically addressing resource constraints and optimizing virtual machine performance.
16. The quality of experience assurance decision support system of claim 15 , wherein determining, by the quality of experience assurance analyzer module, whether the quality of experience assurance analytics indicate that the quality of experience associated with the service provided by the virtual machine has been degraded comprises: determining, by the quality of experience assurance analyzer module, whether a video rebuffering frequency is greater than or equal to a video rebuffering frequency threshold; and if the video rebuffering frequency is greater than or equal to the video rebuffering frequency threshold, determining that the quality of experience associated with the service provided by the virtual machine has been degraded.
This invention relates to a quality of experience (QoE) assurance system for monitoring and evaluating the performance of services delivered by virtual machines. The system addresses the problem of ensuring consistent service quality by detecting degradation in user experience, particularly in video streaming applications. The QoE assurance analyzer module evaluates analytics data to assess whether the service quality has deteriorated. Specifically, the system checks if the video rebuffering frequency exceeds a predefined threshold. If the rebuffering frequency meets or surpasses this threshold, the system concludes that the quality of experience has degraded. This mechanism helps identify performance issues in real-time, allowing for proactive measures to maintain service reliability. The system may also include additional modules for collecting, processing, and analyzing QoE metrics, ensuring comprehensive monitoring of virtual machine performance. By focusing on rebuffering frequency as a key indicator, the invention provides a targeted approach to detecting video streaming disruptions, which are critical for user satisfaction in multimedia applications. The solution is designed to integrate with existing virtual machine environments, enhancing their ability to deliver high-quality services.
17. The quality of experience assurance decision support system of claim 15 , wherein the operations further comprise generating an alert directed to a software-defined network controller, wherein the alert comprises a recommendation that identifies the new network resource for capacity reallocation to accommodate the virtual machine migration.
This invention relates to a quality of experience (QoE) assurance system for software-defined networks (SDNs). The system monitors network performance and dynamically adjusts resources to maintain optimal service quality. A key challenge in SDNs is efficiently managing virtual machine (VM) migrations while ensuring network performance remains stable. The system addresses this by analyzing network conditions and identifying available resources to support VM migrations without degrading QoE. The system includes a decision support module that evaluates network traffic patterns, resource utilization, and QoE metrics. When a VM migration is detected or predicted, the system identifies a new network resource with sufficient capacity to accommodate the migration. It then generates an alert directed to the SDN controller, containing a recommendation for reallocating capacity to the identified resource. This proactive approach prevents performance degradation by ensuring the network can handle the additional load from the migrating VM. The system may also prioritize resources based on factors like latency, bandwidth, and historical performance to optimize the migration process. By automating these decisions, the system reduces manual intervention and improves network efficiency.
18. The quality of experience assurance decision support system of claim 17 , wherein the operations further comprise sending the alert to the software-defined network controller so that the software-defined network controller can initiate the virtual machine migration to the new network resource identified in the recommendation.
This invention relates to a quality of experience (QoE) assurance system for software-defined networks (SDNs). The system monitors network performance and user experience metrics to detect potential issues that could degrade service quality. When a problem is identified, the system generates an alert and provides a recommendation for remediation, such as migrating a virtual machine (VM) to a different network resource to improve performance. The system sends this alert to the SDN controller, which then executes the recommended VM migration to the new network resource. This proactive approach ensures that network performance remains optimal by dynamically adjusting resource allocation based on real-time QoE data. The system integrates with existing SDN infrastructure to automate corrective actions, reducing manual intervention and minimizing service disruptions. The solution is particularly useful in environments where maintaining high-quality user experiences is critical, such as cloud computing, data centers, and enterprise networks. By leveraging SDN capabilities, the system enables efficient and scalable management of network resources to meet evolving QoE requirements.
19. The quality of experience assurance decision support system of claim 15 , wherein identifying the new network resource for capacity reallocation to accommodate the virtual machine migration comprises selecting a hardware host from a resource pool in a server cluster associated with the virtual machine.
The system provides quality of experience (QoE) assurance for virtualized environments by dynamically reallocating network resources to support virtual machine (VM) migrations. The problem addressed is ensuring seamless performance during VM migrations without degrading user experience, which requires real-time resource management to prevent bottlenecks or service disruptions. The system monitors network conditions and VM workloads to detect migration needs, then identifies and reallocates available network resources to accommodate the migration. Specifically, the system selects a hardware host from a resource pool within a server cluster associated with the migrating VM. The selection process considers factors such as host capacity, current load, and network connectivity to ensure optimal resource allocation. By dynamically adjusting resources, the system maintains service quality and minimizes migration-related performance impacts. The solution integrates with existing virtualization platforms to provide automated, data-driven decision support for network resource management during VM migrations. This approach improves efficiency and reliability in cloud and data center environments where VM mobility is critical.
20. The quality of experience assurance decision support system of claim 15 , wherein determining whether the root cause of the quality of experience associated with the service provided by the virtual machine being degraded is due to a capacity reduction further comprises determining capacity utilization of a hardware host that hosts the virtual machine is greater than a designated utilization threshold.
The system provides a quality of experience (QoE) assurance framework for virtualized services, addressing performance degradation in virtual machines (VMs) by identifying root causes. The system monitors service quality metrics and analyzes potential issues, including capacity constraints. When QoE degradation is detected, the system evaluates whether the root cause is due to insufficient hardware resources. Specifically, it assesses the capacity utilization of the physical host machine running the VM. If the host's resource usage exceeds a predefined threshold, the system concludes that the degradation is caused by capacity limitations. This allows for targeted troubleshooting and resource allocation adjustments to restore optimal service performance. The system integrates real-time monitoring, threshold-based analysis, and automated root cause determination to enhance reliability in virtualized environments. By focusing on hardware host capacity as a key factor, the system helps prevent performance bottlenecks and ensures consistent service quality.
Unknown
July 7, 2020
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